Methods and apparatus for data analysis

ABSTRACT

A method and apparatus for testing semiconductors according to various aspects of the present invention comprises a test system comprising composite data analysis element configured to analyze data from more than one dataset. The test system may be configured to provide the data in an output report. The composite data analysis element suitably performs a spatial analysis to identify patterns and irregularities in the composite data set. The composite data analysis element may also operate in conjunction with a various other analysis systems, such as a cluster detection system and an exclusion system, to refine the composite data analysis. The composite may also be merged into other data.

CROSS-REFERENCES TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.10/367,355, filed on Feb. 14, 2003, now U.S. Pat. No. 7,167,811 entitledMETHODS AND APPARATUS FOR DATA ANALYSIS.

FIELD OF THE INVENTION

The invention relates to data analysis.

BACKGROUND OF THE INVENTION

Semiconductor companies test components to ensure that the componentsoperate properly. The test data not only determine whether thecomponents function properly, but also may indicate deficiencies in themanufacturing process. Accordingly, many semiconductor companies mayanalyze the collected data from several different components to identifyproblems and correct them. For example, the company may gather test datafor multiple chips on each wafer among several different lots. This datamay be analyzed to identify common deficiencies or patterns of defectsor identify parts that may exhibit quality and performance issues and toidentify or classify user-defined “good parts”. Steps may then be takento correct the problems. Testing is typically performed before devicepackaging (at wafer level) as well as upon completion of assembly (finaltest).

Gathering and analyzing test data is expensive and time consuming.Automatic testers apply signals to the components and read thecorresponding output signals. The output signals may be analyzed todetermine whether the component is operating properly. Each testergenerates a large volume of data. For example, each tester may perform200 tests on a single component, and each of those tests may be repeated10 times. Consequently, a test of a single component may yield 2000results. Because each tester is testing 100 or more components an hourand several testers may be connected to the same server, an enormousamount of data must be stored. Further, to process the data, the servertypically stores the test data in a database to facilitate themanipulation and analysis of the data. Storage in a conventionaldatabase, however, requires further storage capacity as well as time toorganize and store the data.

The analysis of the gathered data is also difficult. The volume of thedata may demand significant processing power and time. As a result, thedata is not usually analyzed at product run time, but is insteadtypically analyzed between test runs or in other batches.

To alleviate some of these burdens, some companies only sample the datafrom the testers and discard the rest. Analyzing less than all of thedata, however, ensures that the resulting analysis cannot be fullycomplete and accurate. As a result, sampling degrades the completeunderstanding of the test results.

In addition, even when the full set of test data generated by the testeris retained, the sheer volume of the test data presents difficulties inanalyzing the data and generating meaningful results. The data maycontain significant information about the devices, the testing process,and the manufacturing process that may be used to improve production,reliability, and testing. In view of the amount of data, however,isolating and presenting the information to the user or another systemis challenging.

Furthermore, acquiring the test data presents a complex and painstakingprocess. A test engineer prepares a test program to instruct the testerto generate the input signals to the component and receive the outputsignals. The program tends to be very complex to ensure full and properoperation of the component. Consequently, the test program for amoderately complex integrated circuit involves a large number of testsand results. Preparing the program demands extensive design andmodification to arrive at a satisfactory solution, and optimization ofthe program, for example to remove redundant tests or otherwise minimizetest time, requires additional exertion.

SUMMARY OF THE INVENTION

A method and apparatus for testing semiconductors according to variousaspects of the present invention comprises a test system comprisingcomposite data analysis element configured to analyze data from morethan one dataset. The test system may be configured to provide the datain an output report. The composite data analysis element suitablyperforms a spatial analysis to identify patterns and irregularities inthe composite data set. The composite data analysis element may alsooperate in conjunction with a various other analysis systems, such as acluster detection system and an exclusion system, to refine thecomposite data analysis. The composite may also be merged into otherdata.

BRIEF DESCRIPTION OF THE DRAWING

A more complete understanding of the present invention may be derived byreferring to the detailed description and the claims when considered inconnection with the following illustrative figures, which may not be toscale. Like reference numbers refer to similar elements throughout thefigures.

FIG. 1 is a block diagram of a test system according to various aspectsof the present invention and associated functional components;

FIG. 2 is a block diagram of elements for operating the test system;

FIG. 3 illustrates a flow diagram for a configuration element;

FIGS. 4A-C illustrate a flow diagram for a supplemental data analysiselement;

FIG. 5 is a diagram of various sections of a wafer and sectioningtechniques;

FIGS. 6A-B further illustrate a flow diagram for a supplemental dataanalysis element;

FIG. 7 illustrates a flow diagram for an output element;

FIG. 8 is a flow diagram for operation of an exemplary data smoothingsystem according to various aspects of the present invention;

FIG. 9 is a plot of test data for a test of multiple components;

FIG. 10 is a representation of a wafer having multiple devices and aresistivity profile for the wafer;

FIG. 11 is a graph of resistance values for a population of resistors inthe various devices of the wafer of FIG. 10;

FIGS. 12A-B are general and detailed plots, respectively, of raw testdata and outlier detection triggers for the various devices of FIG. 10;

FIG. 13 is a flow diagram of a composite analysis process according tovarious aspects of the present invention;

FIG. 14 is a diagram of a representative data point location on threerepresentative wafers;

FIGS. 15A-C are a flow diagram and a chart relating to a cumulativesquared composite data analysis process;

FIG. 16 is a diagram of an exclusion zone defined on a wafer;

FIGS. 17A-B are a flow diagram of a proximity weighting process;

FIG. 18 is a diagram of a set of data points subjects to proximityweighting;

FIG. 19 is a flow diagram of a cluster detection and filtration process;

FIG. 20 is a diagram of a set of clusters on a wafer subject todetection and filtration;

FIG. 21 is a diagram of a set of data points merged using an absolutemerge process;

FIG. 22 is a diagram of a set of data points merged using an overlapmerge process; and

FIGS. 23 and 24 are diagrams of sets of data points merged usingpercentage overlap merge processes.

Elements in the figures are illustrated for simplicity and clarity andhave not necessarily been drawn to scale. For example, the connectionsand steps performed by some of the elements in the figures may beexaggerated or omitted relative to other elements to help to improveunderstanding of embodiments of the present invention.

DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS

The present invention may be described in terms of functional blockcomponents and various process steps. Such functional blocks and stepsmay be realized by any number of hardware or software componentsconfigured to perform the specified functions. For example, the presentinvention may employ various testers, processors, storage systems,processes, and integrated circuit components, e.g., statistical engines,memory elements, signal processing elements, logic elements, programs,and the like, which may carry out a variety of functions under thecontrol of one or more testers, microprocessors, or other controldevices. In addition, the present invention may be practiced inconjunction with any number of test environments, and each systemdescribed is merely one exemplary application for the invention.Further, the present invention may employ any number of conventionaltechniques for data analysis, component interfacing, data processing,component handling, and the like.

Referring to FIG. 1, a method and apparatus according to various aspectsof the present invention operates in conjunction with a test system 100having a tester 102, such as automatic test equipment (ATE) for testingsemiconductors. In the present embodiment, the test system 100 comprisesa tester 102 and a computer system 108. The test system 100 may beconfigured for testing any components 106, such as semiconductor deviceson a wafer, circuit boards, packaged devices, or other electrical oroptical systems. In the present embodiment, the components 106 comprisemultiple integrated circuit dies formed on a wafer or packagedintegrated circuits or devices.

The tester 102 suitably comprises any test equipment that testscomponents 106 and generates output data relating to the testing. Thetester 102 may comprise a conventional automatic tester, such as aTeradyne tester, and suitably operates in conjunction with otherequipment for facilitating the testing. The tester 102 may be selectedand configured according to the particular components 106 to be testedand/or any other appropriate criteria.

The tester 102 may operate in conjunction with the computer system 108to, for example, program the tester 102, load and/or execute the testprogram, collect data, provide instructions to the tester 102, implementa statistical engine, control tester parameters, and the like. In thepresent embodiment, the computer system 108 receives tester data fromthe tester 102 and performs various data analysis functionsindependently of the tester 102. The computer system 108 also implementsa statistical engine to analyze data from the tester 102. The computersystem 108 may comprise a separate computer, such as a personal computeror workstation, connected to or networked with the tester 102 toexchange signals with the tester 102. In an alternative embodiment, thecomputer system 108 may be omitted from or integrated into othercomponents of the test system 100 and various functions may be performedby other components, such as the tester 102.

The computer system 108 includes a processor 110 and a memory 112. Theprocessor 110 comprises any suitable processor, such as a conventionalIntel, Motorola, or Advanced Micro Devices processor, operating inconjunction with any suitable operating system, such as Windows 98,Windows NT, Unix, or Linux. Similarly, the memory 112 may comprise anyappropriate memory accessible to the processor 110, such as a randomaccess memory (RAM) or other suitable storage system, for storing data.In particular, the memory 112 of the present system includes a fastaccess memory for storing and receiving information and is suitablyconfigured with sufficient capacity to facilitate the operation of thecomputer 108.

In the present embodiment, the memory 112 includes capacity for storingoutput results received from the tester 102 and facilitating analysis ofthe output test data. The memory 112 is configured for fast storage andretrieval of test data for analysis. The memory 112 is suitablyconfigured to store the elements of a dynamic datalog, suitablycomprising a set of information selected by the test system 100 and/orthe operator according to selected criteria and analysis based on thetest results.

For example, the memory 112 suitably stores a component identifier foreach component 106, such as x-y coordinates corresponding to a positionof the component 106 on a wafer map for the tested wafer. Each x-ycoordinate in the memory 112 may be associated with a particularcomponent 106 at the corresponding x-y coordinate on the wafer map. Eachcomponent identifier has one or more fields, and each field corresponds,for example, to a particular test performed on the component 106 at thecorresponding x-y position on the wafer, a statistic related to thecorresponding component 106, or other relevant data. The memory 112 maybe configured to include any data identified by the user as desiredaccording to any criteria or rules.

The computer 108 of the present embodiment also suitably has access to astorage system, such as another memory (or a portion of the memory 112),a hard drive array, an optical storage system, or other suitable storagesystem. The storage system may be local, like a hard drive dedicated tothe computer 108 or the tester 102, or may be remote, such as a harddrive array associated with a server to which the test system 100 isconnected. The storage system may store programs and/or data used by thecomputer 108 or other components of the test system 100. In the presentembodiment, the storage system comprises a database 114 available via aremote server 116 comprising, for example, a main production server fora manufacturing facility. The database 114 stores tester information,such as tester data files, master data files for operating the testsystem 100 and its components, test programs, downloadable instructionsfor the test system 100, and the like. In addition, the storage systemmay comprise complete tester data files, such as historical tester datafiles retained for analysis.

The test system 100 may include additional equipment to facilitatetesting of the components 106. For example, the present test system 100includes a device interface 104, like a conventional device interfaceboard and/or a device handler or prober, to handle the components 106and provide an interface between the components 106 and the tester 102.The test system 100 may include or be connected to other components,equipment, software, and the like to facilitate testing of thecomponents 106 according to the particular configuration, application,environment of the test system 100, or other relevant factors. Forexample, in the present embodiment, the test system 100 is connected toan appropriate communication medium, such as a local area network,intranet, or global network like the internet, to transmit informationto other systems, such as the remote server 116.

The test system 100 may include one or more testers 102 and one or morecomputers 108. For example, one computer 108 may be connected to anappropriate number of, such as up to twenty or more, testers 102according to various factors, such as the system's throughput and theconfiguration of the computer 108. Further, the computer 108 may beseparate from the tester 102, or may be integrated into the tester 102,for example utilizing one or more processors, memories, clock circuits,and the like of the tester 102 itself. In addition, various functionsmay be performed by different computers. For example, a first computermay perform various pre-analysis tasks, several computers may thenreceive the data and perform data analysis, and another set of computersmay prepare the dynamic datalogs and/or other output reports.

A test system 100 according to various aspects of the present inventiontests the components 106 and provides enhanced analysis and testresults. For example, the supplemental analysis may identify incorrect,questionable, or unusual results, repetitive tests, and/or tests with arelatively high probability of failure. The test system 100 may alsoanalyze multiple sets of data, such as data taken from multiple wafersand/or lots of wafers, to generate composite data based on multipledatasets. The operator, such as the product engineer, test engineer,manufacturing engineer, device engineer, or other personnel using thetest data, may then use the results to verify and/or improve the testsystem 100 and or the fabrication system and classify the components106.

The test system 100 according to various aspects of the presentinvention executes an enhanced test process for testing the components106 and collecting and analyzing test data. The test system 100 suitablyoperates in conjunction with a software application executed by thecomputer 108. Referring to FIG. 2, the software application of thepresent embodiment includes multiple elements for implementing theenhanced test process, including a configuration element 202, asupplementary data analysis element 206, and an output element 208. Thetest system 100 may also include a composite analysis element 214 foranalyzing data from more than one dataset. Each element 202, 206, 208,214 suitably comprises a software module operating on the computer 108to perform various tasks. Generally, the configuration element 202prepares test system 100 for testing and analysis. In the supplementarydata analysis element 206, output test data from the tester 102 isanalyzed to generate supplementary test data, suitably at run time andautomatically. The supplementary test data is then transmitted to theoperator or another system, such as the composite analysis element, orthe output element 208. As shown in FIG. 2, the data may also besubstantially concurrently provided to the composite analysis element214 and the output element 208.

The configuration element 202 configures the test system 100 for testingthe components 106 and analyzing the test data. The test system 100suitably uses a predetermined set of initial parameters and, if desired,information from the operator to configure the test system 100. The testsystem 100 is suitably initially configured with predetermined ordefault parameters to minimize operator attendance to the test system100. Adjustments may be made to the configuration by the operator, ifdesired, for example via the computer 108.

Referring to FIG. 3, an exemplary configuration process 300 performed bythe configuration element 202 begins with an initialization procedure(step 302) to set the computer 108 in an initial state. Theconfiguration element 202 then obtains application configurationinformation (step 304), for example from the database 114, for thecomputer 108 and the tester 102. For example, the configuration element202 may access a master configuration file for the enhanced test processand/or a tool configuration file relating to the tester 102. The masterconfiguration file may contain data relating to the proper configurationfor the computer 108 and other components of the test system 100 toexecute the enhanced test process. Similarly, the tool configurationfile suitably includes data relating to the tester 102 configuration,such as connection, directory, IP address, tester node identification,manufacturer, flags, prober identification, or any other pertinentinformation for the tester 102.

The configuration element 202 may then configure the test system 100according to the data contained in the master configuration file and/orthe tool configuration file (step 306). In addition, the configurationelement 202 may use the configuration data to retrieve further relevantinformation from the database 114, such as the tester's 102 identifier(step 308) for associating data like logistics instances for tester datawith the tester 102. The test system 100 information also suitablyincludes one or more default parameters that may be accepted, declined,or adjusted by the operator. For example, the test system 100information may include global statistical process control (SPC) rulesand goals that are submitted to the operator upon installation,configuration, power-up, or other appropriate time for approval and/ormodification. The test system 100 information may also include defaultwafer maps or other files that are suitably configured for each product,wafer, component 106, or other item that may affect or be affected bythe test system 100. The configuration algorithms, parameters, and anyother criteria may be stored in a recipe file for easy access,correlation to specific products and/or tests, and for traceability.

When the initial configuration process is complete, the test system 100commences a test run, for example in conjunction with a conventionalseries of tests, in accordance with a test program. The tester 102suitably executes the test program to apply signals to connections onthe components 106 and read output test data from the components 106.The tester 102 may perform multiple tests on each component 106 on awafer, and each test may be repeated several times on the same component106. Test data from the tester 102 is stored for quick access andsupplemental analysis as the test data is acquired. The data may also bestored in a long-term memory for subsequent analysis and use.

Each test generates at least one result for at least one of thecomponents. Referring to FIG. 9, an exemplary set of test results for asingle test of multiple components comprises a first set of test resultshaving statistically similar values and a second set of test resultscharacterized by values that stray from the first set. Each test resultmay be compared to an upper test limit and a lower test limit. If aparticular result for a component exceeds either limit, the componentmay be classified as a “bad part”.

Some of the test results in the second set that stray from the first setmay exceed the control limits, while others do not. For the presentpurposes, those test results that stray from the first set but do notexceed the control limits or otherwise fail to be detected are referredto as “outliers”. The outliers in the test results may be identified andanalyzed for any appropriate purpose, such as to identify potentiallyunreliable components. The outliers may also be used to identify avarious potential problems and/or improvements in the test andmanufacturing processes.

As the tester 102 generates the test results, the output test data foreach component, test, and repetition is stored by the tester 102 in atester data file. The output test data received from each component 106is analyzed by the tester 102 to classify the performance of thecomponent 106, for example by comparison to the upper and lower testlimits, and the results of the classification are also stored in thetester data file. The tester data file may include additionalinformation as well, such as logistics data and test programidentification data. The tester data file is then provided to thecomputer 108 in an output file, such as a standard tester data format(STDF) file, and stored in memory. The tester data file may also bestored in the storage system for longer term storage for later analysis,such as by the composite analysis element 214.

When the computer 108 receives the tester data file, the supplementarydata analysis element 206 analyzes the data to provide enhanced outputresults. The supplementary data analysis element 206 may provide anyappropriate analysis of the tester data to achieve any suitableobjective. For example, the supplementary data analysis element 206 mayimplement a statistical engine for analyzing the output test data at runtime and identifying data and characteristics of the data of interest tothe operator. The data and characteristics identified may be stored,while data that is not identified may be otherwise disposed of, such asdiscarded.

The supplementary data analysis element 206 may, for example, calculatestatistical figures according to the data and a set of statisticalconfiguration data. The statistical configuration data may call for anysuitable type of analysis according to the needs of the test system 100and/or the operator, such as statistical process control, outlieridentification and classification, signature analyses, and datacorrelation. Further, the supplementary data analysis element 206suitably performs the analysis at run time, i.e., within a matter ofseconds or minutes following generation of the test data. Thesupplementary data analysis element 206 may also perform the analysisautomatically with minimal intervention from the operator and/or testengineer.

In the present test system 100, after the computer 108 receives andstores the tester data file, the supplementary data analysis element 206performs various preliminary tasks to prepare the computer 108 foranalysis of the output test data and facilitate generation ofsupplementary data and preparation of an output report. Referring now toFIGS. 4A-C, in the present embodiment, the supplementary data analysiselement 206 initially copies the tester data file to a tool inputdirectory corresponding to the relevant tester 102 (step 402). Thesupplementary data analysis element 206 also retrieves configurationdata to prepare the computer 108 for supplementary analysis of theoutput test data.

The configuration data suitably includes a set of logistics data thatmay be retrieved from the tester data file (step 404). The supplementarydata analysis element 206 also creates a logistics reference (step 406).The logistics reference may include tester 102 information, such as thetester 102 information derived from the tool configuration file. Inaddition, the logistics reference is assigned an identification.

The configuration data may also include an identifier for the testprogram that generated the output test data. The test program may beidentified in any suitable manner, such as looking it up in the database114 (step 408), by association with the tester 102 identification, orreading it from the master configuration file. If no test programidentification can be established (step 410), a test programidentification may be created and associated with the testeridentification (step 412).

The configuration data further identifies the wafers in the test run tobe processed by the supplementary data analysis element 206, if fewerthan all of the wafers. In the present embodiment, the supplementarydata analysis element 206 accesses a file indicating which wafers are tobe analyzed (step 414). If no indication is provided, the computer 108suitably defaults to analyzing all of the wafers in the test run.

If the wafer for the current test data file is to be analyzed (step416), the supplementary data analysis element 206 proceeds withperforming the supplementary data analysis on the test data file for thewafer. Otherwise, the supplementary data analysis element 206 waits foror accesses the next test data file (step 418).

The supplementary data analysis element 206 may establish one or moresection groups to be analyzed for the various wafers to be tested (step420). To identify the appropriate section group to apply to the outputtest data, the supplementary data analysis element 206 suitablyidentifies an appropriate section group definition, for exampleaccording to the test program and/or the tester identification. Eachsection group includes one or more section arrays, and each sectionarray includes one or more sections of the same section types.

Section types comprise various sorts of component 106 groups positionedin predetermined areas of the wafer. For example, referring to FIG. 5, asection type may include a row 502, a column 504, a stepper field 506, acircular band 508, a radial zone 510, a quadrant 512, or any otherdesired grouping of components. Different section types may be usedaccording to the configuration of the components, such as order ofcomponents processed, sections of a tube, or the like. Such groups ofcomponents 106 are analyzed together to identify, for example, commondefects or characteristics that may be associated with the group. Forexample, if a particular portion of the wafer does not conduct heat likeother portions of the wafer, the test data for a particular group ofcomponents 106 may reflect common characteristics or defects associatedwith the uneven heating of the wafer.

Upon identifying the section group for the current tester data file, thesupplemental data analysis element 206 retrieves any further relevantconfiguration data, such as control limits and enable flags for the testprogram and/or tester 102 (step 422). In particular, the supplementaldata analysis element 206 suitably retrieves a set of desired statisticsor calculations associated with each section array in the section group(step 423). Desired statistics and calculations may be designated in anymanner, such as by the operator or retrieved from a file. Further, thesupplemental data analysis element 206 may also identify one or moresignature analysis algorithms (step 424) for each relevant section typeor other appropriate variation relating to the wafer and retrieve thesignature algorithms from the database 114 as well.

All of the configuration data may be provided by default orautomatically accessed by the configuration element 202 or thesupplemental data analysis element 206. Further, the configurationelement 202 and the supplemental data analysis element 206 of thepresent embodiment suitably allow the operator to change theconfiguration data according to the operator's wishes or the test system100 requirements. When the configuration data have been selected, theconfiguration data may be associated with relevant criteria and storedfor future use as default configuration data. For example, if theoperator selects a certain section group for a particular kind ofcomponents 106, the computer 108 may automatically use the same sectiongroup for all such components 106 unless instructed otherwise by theoperator.

The supplemental data analysis element 206 also provides forconfiguration and storage of the tester data file and additional data.The supplemental data analysis element 206 suitably allocates memory(step 426), such as a portion of the memory 112, for the data to bestored. The allocation suitably provides memory for all of the data tobe stored by the supplemental data analysis element 206, includingoutput test data from the tester data file, statistical data generatedby the supplemental data analysis element 206, control parameters, andthe like. The amount of memory allocated may be calculated according to,for example, the number of tests performed on the components 106, thenumber of section group arrays, the control limits, statisticalcalculations to be performed by the supplementary data analysis element206, and the like.

When all of the configuration data for performing the supplementaryanalysis are ready and upon receipt of the output test data, thesupplementary data analysis element 206 loads the relevant test datainto memory (step 428) and performs the supplementary analysis on theoutput test data. The supplementary data analysis element 206 mayperform any number and types of data analyses according to thecomponents 106, configuration of the test system 100, desires of theoperator, or other relevant criteria. The supplemental data analysiselement 206 may be configured to analyze the sections for selectedcharacteristics identifying potentially defective components 106 andpatterns, trends, or other characteristics in the output test data thatmay indicate manufacturing concerns or flaws.

The present supplementary data analysis element 206, for example,smoothes the output test data, calculates and analyzes variousstatistics based on the output test data, and identifies data and/orcomponents 106 corresponding to various criteria. The presentsupplementary data analysis element 206 may also classify and correlatethe output test data to provide information to the operator and/or testengineer relating to the components 106 and the test system 100. Forexample, the present supplementary data analysis element 206 may performoutput data correlations, for example to identify potentially related orredundant tests, and outlier incidence analyses to identify tests havingfrequent outliers.

The supplementary data analysis element 206 may include a smoothingsystem to initially process the tester data to smooth the data andassist in the identification of outliers (step 429). The smoothingsystem may also identify significant changes in the data, trends, andthe like, which may be provided to the operator by the output element208. The smoothing system is suitably implemented, for example, as aprogram operating on the computer system 108. The smoothing systemsuitably comprises multiple phases for smoothing the data according tovarious criteria. The first phase may include a basic smoothing process.The supplemental phases conditionally provide for enhanced trackingand/or additional smoothing of the test data.

The smoothing system suitably operates by initially adjusting an initialvalue of a selected tester datum according to a first smoothingtechnique, and supplementarily adjusting the value according to a secondsmoothing technique if at least one of the initial value and theinitially adjusted value meets a threshold. The first smoothingtechnique tends to smooth the data. The second smoothing technique alsotends to smooth the data and/or improve tracking of the data, but in adifferent manner from the first smoothing technique. Further, thethreshold may comprise any suitable criteria for determining whether toapply supplemental smoothing. The smoothing system suitably compares aplurality of preceding adjusted data to a plurality of preceding rawdata to generate a comparison result, and applies a second smoothingtechnique to the selected datum to adjust the value of the selecteddatum according to whether the comparison result meets a firstthreshold. Further, the smoothing system suitably calculates a predictedvalue of the selected datum, and may apply a third smoothing techniqueto the selected datum to adjust the value of the selected datumaccording to whether the predicted value meets a second threshold.

Referring to FIG. 8, a first smoothed test data point is suitably setequal to a first raw test data point (step 802) and the smoothing systemproceeds to the next raw test data point (step 804). Before performingsmoothing operations, the smoothing system initially determines whethersmoothing is appropriate for the data point and, if so, performs a basicsmoothing operation on the data. Any criteria may be applied todetermine whether smoothing is appropriate, such as according to thenumber of data points received, the deviation of the data point valuesfrom a selected value, or comparison of each data point value to athreshold. In the present embodiment, the smoothing system performs athreshold comparison. The threshold comparison determines whether datasmoothing is appropriate. If so, the initial smoothing process issuitably configured to proceed to an initial smoothing of the data.

More particularly, in the present embodiment, the process starts with aninitial raw data point R₀, which is also designated as the firstsmoothed data point S₀. As additional data points are received andanalyzed, a difference between each raw data point (R_(n)) and apreceding smoothed data point (S_(n-1)) is calculated and compared to athreshold (T₁) (step 806). If the difference between the raw data pointR_(n) and the preceding smoothed data point S_(n-1) exceeds thethreshold T₁, it is assumed that the exceeded threshold corresponds to asignificant departure from the smoothed data and indicates a shift inthe data. Accordingly, the occurrence of the threshold crossing may benoted and the current smoothed data point S_(n) is set equal to the rawdata point R_(n) (step 808). No smoothing is performed, and the processproceeds to the next raw data point.

If the difference between the raw data point and the preceding smootheddata point does not exceed the threshold T₁, the process calculates acurrent smoothed data point S_(n) in conjunction with an initialsmoothing process (step 810). The initial smoothing process provides abasic smoothing of the data. For example, in the present embodiment, thebasic smoothing process comprises a conventional exponential smoothingprocess, such as according to the following equation:S _(n)=(R _(n) −S _(n-1))*M ₁ +S _(n-1)

where M₁ is a selected smoothing coefficient, such as 0.2 or 0.3.

The initial smoothing process suitably uses a relatively low coefficientM₁ to provide a significant amount of smoothing for the data. Theinitial smoothing process and coefficients may be selected according toany criteria and configured in any manner, however, according to theapplication of the smoothing system, the data processed, requirementsand capabilities of the smoothing system, and/or any other criteria. Forexample, the initial smoothing process may employ random, random walk,moving average, simple exponential, linear exponential, seasonalexponential, exponential weighted moving average, or any otherappropriate type of smoothing to initially smooth the data.

The data may be further analyzed for and/or subjected to smoothing.Supplementary smoothing may be performed on the data to enhance thesmoothing of the data and/or improve the tracking of the smoothed datato the raw data. Multiple phases of supplementary smoothing may also beconsidered and, if appropriate, applied. The various phases may beindependent, interdependent, or complementary. In addition, the data maybe analyzed to determine whether supplementary smoothing is appropriate.

In the present embodiment, the data is analyzed to determine whether toperform one or more additional phases of smoothing. The data is analyzedaccording to any appropriate criteria to determine whether supplementalsmoothing may be applied (step 812). For example, the smoothing systemidentify trends in the data, such as by comparing a plurality ofadjusted data points and raw data points for preceding data andgenerating a comparison result according to whether substantially all ofthe preceding adjusted data share a common relationship (such as lessthan, greater than, or equal to) with substantially all of thecorresponding raw data.

The smoothing system of the present embodiment compares a selectednumber P₂ of raw data points to an equal number of smoothed data points.If the values of all of the P₂ raw data points exceed (or are equal to)the corresponding smoothed data points, or if all raw data points areless than (or equal to) the corresponding smoothed data points, then thesmoothing system may determine that the data is exhibiting a trend andshould be tracked more closely. Accordingly, the occurrence may be notedand the smoothing applied to the data may be changed by applyingsupplementary smoothing. If, on the other hand, neither of thesecriteria is satisfied, then the current smoothed data point remains asoriginally calculated and the relevant supplementary data smoothing isnot applied.

In the present embodiment, the criterion for comparing the smoothed datato the raw data is selected to identify a trend in the data behind whichthe smoothed data may be lagging. Accordingly, the number of points P₂may be selected according to the desired sensitivity of the system tochanging trends in the raw data.

The supplementary smoothing changes the effect of the overall smoothingaccording to the data analysis. Any appropriate supplementary smoothingmay be applied to the data to more effectively smooth the data or tracka trend in the data. For example, in the present embodiment, if the dataanalysis indicates a trend in the data that should be tracked moreclosely, then the supplementary smoothing may be applied to reduce thedegree of smoothing initially applied so that the smoothed data moreclosely tracks the raw data (step 814).

In the present embodiment, the degree of smoothing is reduced byrecalculating the value for the current smoothed data point using areduced degree of smoothing. Any suitable smoothing system may be usedto more effectively track the data or otherwise respond to the resultsof the data analysis. In the present embodiment, another conventionalexponential smoothing process is applied to the data using a highercoefficient M₂:S _(n)=(R _(n) −S _(n-1))*M ₂ +S _(n-1)

The coefficients M₁ and M₂ may be selected according to the desiredsensitivity of the system, both in the absence (M₁) and the presence(M₂) of trends in the raw data. In various applications, for example,the value of M₁ may be higher than the value of M₂.

The supplementary data smoothing may include additional phases as well.The additional phases of data smoothing may similarly analyze the datain some manner to determine whether additional data smoothing should beapplied. Any number of phases and types of data smoothing may be appliedor considered according to the data analysis.

For example, in the present embodiment, the data may be analyzed andpotentially smoothed for noise control, such as using a predictiveprocess based on the slope, or trend, of the smoothed data. Thesmoothing system computes a slope (step 816) based on a selected numberP₃ of smoothed data points preceding the current data point according toany appropriate process, such as line regression, N-points centered, orthe like. In the present embodiment, the data smoothing system uses a“least squares fit through line” process to establish a slope of thepreceding P₃ smoothed data points.

The smoothing system predicts a value of the current smoothed data pointaccording to the calculated slope. The system then compares thedifference between the previously calculated value for the currentsmoothed data point (S_(n)) to the predicted value for the currentsmoothed data point to a range number (R₃) (step 818). If the differenceis greater than the range R₃, then the occurrence may be noted and thecurrent smoothed data point is not adjusted. If the difference is withinthe range R₃, then the current smoothed data point is set equal to thedifference between the calculated current smoothed data point (S_(n))and the predicted value for the current smoothed data point (S_(n-pred))multiplied by a third multiplier M₃ and added to the original value ofthe current smoothed data point (step 820). The equation:S _(n)=(S _(n-pred) −S _(n))*M ₃ +S _(n)

Thus, the current smoothed data point is set according to a modifieddifference between the original smoothed data point and the predictedsmoothed data point, but reduced by a certain amount (when M₃ is lessthan 1). Applying the predictive smoothing tends to reducepoint-to-point noise sensitivity during relatively flat (or otherwisenon-trending) portions of the signal. The limited application of thepredictive smoothing process to the smoothed data points ensures thatthe calculated average based on the slope does not affect the smootheddata when significant changes are occurring in the raw data, i.e., whenthe raw data signal is not relatively flat.

After smoothing the data, the supplementary data analysis element 206may proceed with further analysis of the tester data. For example, thesupplementary data analysis element 206 may conduct statistical processcontrol (SPC) calculations and analyses on the output test data. Moreparticularly, referring again to FIGS. 4A-C, the supplemental dataanalysis element 206 may calculate and store desired statistics for aparticular component, test, and/or section (step 430). The statisticsmay comprise any statistics useful to the operator or the test system100, such as SPC figures that may include averages, standard deviations,minima, maxima, sums, counts, Cp, Cpk, or any other appropriatestatistics.

The supplementary data analysis element 206 also suitably performs asignature analysis to dynamically and automatically identify trends andanomalies in the data, for example according to section, based on acombination of test results for that section and/or other data, such ashistorical data (step 442). The signature analysis identifies signaturesand applies a weighting system, suitably configured by the operator,based on any suitable data, such as the test data or identification ofdefects. The signature analysis may cumulatively identify trends andanomalies that may correspond to problem areas or other characteristicsof the wafer or the fabrication process. Signature analysis may beconducted for any desired signatures, such as noise peaks, waveformvariations, mode shifts, and noise. In the present embodiment, thecomputer 108 suitably performs the signature analysis on the output testdata for each desired test in each desired section.

In the present embodiment, a signature analysis process may be performedin conjunction with the smoothing process. As the smoothing processanalyzes the tester data, results of the analysis indicating a trend oranomaly in the data are stored as being indicative of a change in thedata or an outlier that may be of significance to the operator and/ortest engineer. For example, if a trend is indicated by a comparison ofsets of data in the smoothing process, the occurrence of the trend maybe noted and stored. Similarly, if a data point exceeds the threshold T₁in the data smoothing process, the occurrence may be noted and storedfor later analysis and/or inclusion in the output report.

For example, referring to FIGS. 6A-B, a signature analysis process 600may initially calculate a count (step 602) for a particular set of testdata and control limits corresponding to a particular section and test.The signature analysis process then applies an appropriate signatureanalysis algorithm to the data points (step 604). The signature analysisis performed for each desired signature algorithm, and then to each testand each section to be analyzed. Errors identified by the signatureanalysis, trend results, and signature results are also stored (step606). The process is repeated for each signature algorithm (step 608),test (step 610), and section (step 612). Upon completion, thesupplementary data analysis element 206 records the errors (step 614),trend results (step 616), signature results (step 618), and any otherdesired data in the storage system.

Upon identification of each relevant data point, such as outliers andother data of importance identified by the supplementary analysis, eachrelevant data point may be associated with a value identifying therelevant characteristics (step 444). For example, each relevantcomponents or data point may be associated with a series of values,suitably expressed as a hexadecimal figure, corresponding to the resultsof the supplementary analysis relating to the data point. Each value mayoperate as a flag or other designator of a particular characteristic.For example, if a particular data point has failed a particular testcompletely, a first flag in the corresponding hexadecimal value may beset. If a particular data point is the beginning of a trend in the data,another flag may be set. Another value in the hexadecimal figure mayinclude information relating to the trend, such as the duration of thetrend in the data.

The supplementary data analysis element 206 may also be configured toclassify and correlate the data (step 446). For example, thesupplementary data analysis element 206 may utilize the information inthe hexadecimal figures associated with the data points to identify thefailures, outliers, trends, and other features of the data. Thesupplementary data analysis element 206 also suitably appliesconventional correlation techniques to the data, for example to identifypotentially redundant or related tests.

The computer 108 may perform additional analysis functions upon thegenerated statistics and the output test data, such as automaticallyidentifying and classifying outliers (step 432). Analyzing each relevantdatum according to the selected algorithm suitably identifies theoutliers. If a particular algorithm is inappropriate for a set of data,the supplementary data analysis element 206 may be configured toautomatically abort the analysis and select a different algorithm.

The supplementary data analysis element 206 may operate in any suitablemanner to designate outliers, such as by comparison to selected valuesand/or according to treatment of the data in the data smoothing process.For example, an outlier identification element according to variousaspects of the present invention initially automatically calibrates itssensitivity to outliers based on selected statistical relationships foreach relevant datum (step 434). Some of these statistical relationshipsare then compared to a threshold or other reference point, such as thedata mode, mean, or median, or combinations thereof, to define relativeoutlier threshold limits. In the present embodiment, the statisticalrelationships are scaled, for example by one, two, three, and sixstandard deviations of the data, to define the different outlieramplitudes (step 436). The output test data may then be compared to theoutlier threshold limits to identify and classify the output test dataas outliers (step 438).

The supplementary data analysis element 206 stores the resultingstatistics and outliers in memory and identifiers, such as the x-y wafermap coordinates, associated with any such statistics and outliers (step440). Selected statistics, outliers, and/or failures may also triggernotification events, such as sending an electronic message to anoperator, triggering a light tower, stopping the tester 102, ornotifying a server.

In the present embodiment, the supplementary data analysis element 206includes a scaling element 210 and an outlier classification element212. The scaling element 210 is configured to dynamically scale selectedcoefficients and other values according to the output test data. Theoutlier classification element 212 is configured to identify and/orclassify the various outliers in the data according to selectedalgorithms.

More particularly, the scaling element of the present embodimentsuitably uses various statistical relationships for dynamically scalingoutlier sensitivity and smoothing coefficients for noise filteringsensitivity. The scaling coefficients are suitably calculated by thescaling element and used to modify selected outlier sensitivity valuesand smoothing coefficients. Any appropriate criteria, such as suitablestatistical relationships, may be used for scaling. For example, asample statistical relationship for outlier sensitivity scaling isdefined as:(√{square root over (1+Natural Log_(Cpk) ²)})

Another sample statistical relationship for outlier sensitivity andsmoothing coefficient scaling is defined as:(√{square root over (1+Natural Log_(Cpk) ²)})*Cpm

Another sample statistical relationship for outlier sensitivity andsmoothing coefficient scaling is defined as:

$\frac{\left( {\sigma*{Cpk}} \right)}{\left( {{Max} - {Min}} \right)},{{{where}\mspace{14mu}\sigma} = {{datum}\mspace{14mu}{Standard}\mspace{14mu}{Deviation}}}$

A sample statistical relationship used in multiple algorithms forsmoothing coefficient scaling is:

${\frac{\sigma}{\mu}*10},$where σ=datum Standard Deviation and μ=datum Mean

Another sample statistical relationship used in multiple algorithms forsmoothing coefficient scaling is:

${\frac{\sigma^{2}}{\mu^{2}}*10},$where σ=datum Standard Deviation and μ=datum Mean

The outlier classification element is suitably configured to identifyand/or classify components 106, output test data, and analysis resultsaccording to any suitable algorithm the outliers in the output testdata. The outlier classification element may also identify and classifyselected outliers and components 106 according to the test output testresults and the information generated by the supplementary analysiselement 206. For example, the outlier classification element is suitablyconfigured to classify the components 106 into critical/marginal/goodpart categories, for example in conjunction with user-defined criteria;user-defined good/bad spatial patterns recognition; classification ofpertinent data for tester data compression; test setup in-situsensitivity qualifications and analyses; tester yield leveling analyses;dynamic wafer map and/or test strip mapping for part dispositions anddynamic retest; or test program optimization analyses. The outlierclassification element may classify data in accordance with conventionalSPC control rules, such as Western Electric rules or Nelson rules, tocharacterize the data.

The outlier classification element suitably classifies the data using aselected set of classification limit calculation methods. Anyappropriate classification methods may be used to characterize the dataaccording to the needs of the operator. The present outlierclassification element, for example, classifies outliers by comparingthe output test data to selected thresholds, such as valuescorresponding to one, two, three, and six statistically scaled standarddeviations from a threshold, such as the data mean, mode, and/or median.The identification of outliers in this manner tends to normalize anyidentified outliers for any test regardless of datum amplitude andrelative noise.

The outlier classification element analyzes and correlates thenormalized outliers and/or the raw data points based on user-definedrules. Sample user-selectable methods for the purpose of part andpattern classification based on identified outliers are as follows:

Cumulative Amplitude, Cumulative Count Method:

${Count}_{LIMIT} = {\mu_{OverallOutlierCount} + \left( \frac{3*\left( \sigma_{OverallOutlierCount}^{2} \right)}{\left( {{Max}_{OverallOutlierCount} - {Min}_{OverallOutlierCount}} \right)} \right)}$${NormalizedOutlierAmplitude}_{LIMIT} = {\mu_{{Overall}\mspace{11mu}{Normalized}\mspace{11mu}{Outlier}\mspace{11mu}{Amplitude}} + \left( \frac{3*\left( \sigma_{{Overall}\mspace{14mu}{Normalized}\mspace{14mu}{Outlier}\mspace{14mu}{Amplitude}}^{2} \right)}{\begin{pmatrix}{{Max}_{{Overall}\mspace{11mu}{Normalized}\mspace{11mu}{Outlier}\mspace{11mu}{Amplitude}} -} \\{Min}_{{Overall}\mspace{11mu}{Normalized}\mspace{11mu}{Outlier}\mspace{11mu}{Amplitude}}\end{pmatrix}} \right)}$

Classification Rules:Part_(CRITICAL)=True,If└(Part_(Cumlative Outlier Count)>Count_(LIMIT))AND(Part_(Cumlative Normalized Outlier Amplitude)>NormalizedOutlierAmplitude_(LIMIT))┘Part_(MARGINAL:HighAmplitude)=True,If└(Part_(Cumlative Normalized Outlier Amplitude)>NormalizedOutlierAmplitude_(LIMIT))┘Part_(MARGINAL:HighCount)=True,If(Part_(CumlativeOutlierCount)>Count_(LIMIT))

Cumulative Amplitude Squared, Cumulative Count Squared Method:

${Count}_{{LIMIT}^{2}} = {\mu_{{OverallOutlierCount}^{2}} + \left( \frac{3*\left( \sigma_{{OverallOutlierCount}^{2}}^{2} \right)}{\left( {{Max}_{{OverallOutlierCount}^{2}} - {Min}_{{OverallOutlierCount}^{2}}} \right)} \right)}$${{Normalized}\mspace{14mu}{OutlierAmplitude}_{{LIMIT}^{2}}} = {\mu_{{OverallNormalizedOutlierAmplitude}^{2}} + \left( \frac{3*\left( \sigma_{{Overall}\mspace{11mu}{NormalizedOutlier}\mspace{11mu}{Amplitude}^{2}}^{2} \right)}{\begin{pmatrix}{{Max}_{{OverallNormalizedOutlierAmplitude}^{2}} -} \\{Min}_{{OverallNormalizedOutlierAmplitude}^{2}}\end{pmatrix}} \right)}$

Classification Rules:Part_(CRITICAL)=True,If└(Part_(CumlativeOutlierCount) ₂ >Count_(LIMIT) ₂)AND(Part_(CumlativeNormalizedOutlierAmplitude) ₂ >NormalizedOutlierAmplitude_(LIMIT) ₂ )┘Part_(MARGINAL:HighAmplitude)=True,If└(Part_(CumlativeNormalizedOutlierAmplitude)₂ >Normalized OutlierAmplitude_(LIMIT) ₂ )┘Part_(MARGINAL:HighCount)=True,If└(Part_(Cumlative Outlier Count) ₂>Count_(LIMIT) ₂ )┘

N-points Method:

The actual numbers and logic rules used in the following examples can becustomized by the end user per scenario (test program, test node,tester, prober, handler, test setup, etc.). σ in these examples=σrelative to datum mean, mode, and/or median based on datum standarddeviation scaled by key statistical relationships.Part_(CRITICAL)=True,If[((Part_(COUNT:6σ)+Part_(COUNT:3σ))≧2)OR((Part_(COUNT:2σ)+Part_(COUNT:1σ))≧6)]Part_(CRITICAL)=True,If[((Part_(COUNT:6σ)+Part_(COUNT:3σ))≧1)AND((Part_(COUNT:2σ)+Part_(COUNT:1σ))≧3)]Part_(MARGINAL)=True,If[((Part_(COUNT:6σ)+Part_(COUNT:3σ)+Part_(COUNT:2σ)+Part_(COUNT:1σ))≧3)]Part_(NOISY)=True,If[((Part_(COUNT:6σ)+Part_(COUNT:3σ)+Part_(COUNT:2σ)+Part_(COUNT:1σ))≧1)]

The supplementary data analysis element 206 may be configured to performadditional analysis of the output test data and the informationgenerated by the supplementary data analysis element 206. For example,the supplementary data analysis element 206 may identify tests havinghigh incidences of failure or outliers, such as by comparing the totalor average number of failures, outliers, or outliers in a particularclassification to one or more threshold values.

The supplementary data analysis element 206 may also be configured tocorrelate data from different tests to identify similar or dissimilartrends, for example by comparing cumulative counts, outliers, and/orcorrelating outliers between wafers or other data sets. Thesupplementary data analysis element 206 may also analyze and correlatedata from different tests to identify and classify potential criticaland/or marginal and/or good parts on the wafer. The supplementary dataanalysis element 206 may also analyze and correlate data from differenttests to identify user-defined good part patterns and/or bad partpatterns on a series of wafers for the purposes of dynamic test timereduction.

The supplementary data analysis element 206 is also suitably configuredto analyze and correlate data from different tests to identifyuser-defined pertinent raw data for the purposes of dynamicallycompressing the test data into memory. The supplementary data analysiselement may also analyze and correlate statistical anomalies and testdata results for test node in-situ setup qualification and sensitivityanalysis. Further, the supplementary data analysis element maycontribute to test node yield leveling analysis, for example byidentifying whether a particular test node may be improperly calibratedor otherwise producing inappropriate results. The supplementary dataanalysis element may moreover analyze and correlate the data for thepurposes of test program optimization including, but not limited to,automatic identification of redundant tests using correlated results andoutlier analysis and providing additional data for use in analysis. Thesupplementary data analysis element is also suitably configured toidentify critical tests, for example by identifying regularly failed oralmost failed tests, tests that are almost never-fail, and/or testsexhibiting a very low Cpk.

The supplementary data analysis may also provide identification of testsampling candidates, such as tests that are rarely or never failed or inwhich outliers are never detected. The supplementary data analysiselement may also provide identification of the best order test sequencebased on correlation techniques, such as conventional correlationtechniques, combined with analysis and correlation of identifiedoutliers and/or other statistical anomalies, number of failures,critical tests, longest/shortest tests, or basic functionality issuesassociated with failure of the test.

The supplementary data analysis may also provide identification ofcritical, marginal, and good parts as defined by sensitivity parametersin a recipe configuration file. Part identification may providedisposition/classification before packaging and/or shipping the partthat may represent a reliability risk, and/or test time reductionthrough dynamic probe mapping of bad and good parts during wafer probe.Identification of these parts may be represented and output in anyappropriate manner, for example as good and bad parts on a dynamicallygenerated prober control map (for dynamic mapping), a wafer map used foroffline inking equipment, a test strip map for strip testing at finaltest, a results file, and/or a database results table.

Supplemental data analysis at the cell controller level tends toincrease quality control at the probe, and this final test yields. Inaddition, quality issues may be identified at product run time, notlater. Furthermore, the supplemental data analysis and signatureanalysis tends to improve the quality of data provided to the downstreamand offline analysis tools, as well as test engineers or otherpersonnel, by identifying outliers. For example, the computer 108 mayinclude information on the wafer map identifying a group of componentshaving signature analyses indicating a fault in the manufacturingprocess. Thus, the signature analysis system may identify potentiallydefective goods that went undetected using conventional test analysis.

Referring now to FIG. 10, an array of semiconductor devices arepositioned on a wafer. In this wafer, the general resistivity ofresistor components in the semiconductor devices varies across thewafer, for example due to uneven deposition of material or treatment ofthe wafer. The resistance of any particular component, however, may bewithin the control limits of the test. For example, the targetresistance of a particular resistor component may be 1000Ω+/−10%. Nearthe ends of the wafer, the resistances of most of the resistorsapproach, but do not exceed, the normal distribution range of 900Ω and1100Ω (FIG. 11).

Components on the wafer may include defects, for example due to acontaminant or imperfection in the fabrication process. The defect mayincrease the resistance of resistors located near the low-resistivityedge of the wafer to 1080Ω. The resistance is well over the 1000Ωexpected for a device near the middle of the wafer, but is still wellwithin the normal distribution range.

Referring to FIGS. 12A-B, the raw test data for each component may beplotted. The test data exhibits considerable variation, due in part tothe varying resistivity among components on the wafer as the proberindexes across rows or columns of devices. The devices affected by thedefect are not easily identifiable based on visual examination of thetest data or comparison to the test limits.

When the test data is processed according to various aspects of thepresent invention, the devices affected by the defect may be associatedwith outliers in the test data. The test data is largely confined to acertain range of values. The data associated with the defects, however,is unlike the data for the surrounding components. Accordingly, the dataillustrates the departure from the values associated with thesurrounding devices without the defect. The outlier classificationelement may identify and classify the outliers according to themagnitude of the departure of the outlier data from the surroundingdata.

The output element 208 collects data from the test system 100, suitablyat run time, and provides an output report to a printer, database,operator interface, or other desired destination. Any form, such asgraphical, numerical, textual, printed, or electronic form, may be usedto present the output report for use or subsequent analysis. The outputelement 208 may provide any selected content, including selected outputtest data from the tester 102 and results of the supplementary dataanalysis.

In the present embodiment, the output element 208 suitably provides aselection of data from the output test data specified by the operator aswell as supplemental data at product run time via the dynamic datalog.Referring to FIG. 7, the output element 208 initially reads a samplingrange from the database 114 (step 702). The sampling range identifiespredetermined information to be included in the output report. In thepresent embodiment, the sampling range identifies components 106 on thewafer selected by the operator for review. The predetermined componentsmay be selected according to any criteria, such as data for variouscircumferential zones, radial zones, random components, or individualstepper fields. The sampling range comprises a set of x-y coordinatescorresponding to the positions of the predetermined components on thewafer or an identified portion of the available components in a batch.

The output element 208 may also be configured to include informationrelating to the outliers, or other information generated or identifiedby the supplementary data analysis element, in the dynamic datalog (step704). If so configured, the identifiers, such as x-y coordinates, foreach of the outliers are assembled as well. The coordinates for theoperator-selected components and the outliers are merged into thedynamic datalog (step 706), which in the current embodiment is in theformat of the native tester data output format. Merging resulting datainto the dynamic datalog facilitates compression of the original datainto summary statistics and critical raw data values into a smallernative tester data file, reducing data storage requirements withoutcompromising data integrity for subsequent customer analysis. The outputelement 208 retrieves selected information, such as the raw test dataand one or more data from the supplementary data analysis element 206,for each entry in the merged x-y coordinate array of the dynamic datalog(step 708).

The retrieved information is then suitably stored in an appropriateoutput report (step 710). The report may be prepared in any appropriateformat or manner. In the present embodiment, the output report suitablyincludes the dynamic datalog having a wafer map indicating the selectedcomponents on the wafer and their classification. Further, the outputelement 208 may superimpose wafer map data corresponding to outliers onthe wafer map of the preselected components. Additionally, the outputelement may include only the outliers from the wafer map or batch as thesampled output. The output report may also include a series of graphicalrepresentation of the data to highlight the occurrence of outliers andcorrelations in the data. The output report may further includerecommendations and supporting data for the recommendations. Forexample, if two tests appear to generate identical sets of failuresand/or outliers, the output report may include a suggestion that thetests are redundant and recommend that one of the tests be omitted fromthe test program. The recommendation may include a graphicalrepresentation of the data showing the identical results of the tests.

The output report may be provided in any suitable manner, for exampleoutput to a local workstation, sent to a server, activation of an alarm,or any other appropriate manner (step 712). In one embodiment, theoutput report may be provided off-line such that the output does notaffect the operation of the system or transfer to the main server. Inthis configuration, the computer 108 copies data files, performs theanalysis, and generates results, for example for demonstration orverification purposes.

In addition to the supplementary analysis of the data on each wafer, atesting system 100 according to various aspects of the present inventionmay also perform composite analysis of the data and generate additionaldata to identify patterns and trends over multiple datasets, such asusing multiple wafers and/or lots. Composite analysis is suitablyperformed to identify selected characteristics, such as patterns orirregularities, among multiple datasets. For example, multiple datasetsmay be analyzed for common characteristics that may represent patterns,trends, or irregularities over two or more datasets.

The composite analysis may comprise any appropriate analysis foranalyzing test data for common characteristics among the datasets, andmay be implemented in any suitable manner. For example, in the presenttesting system 100, the composite analysis element 214 performscomposite analysis of data derived from multiple wafers and/or lots. Thetest data for each wafer, lot, or other grouping forms a dataset. Thecomposite analysis element 214 is suitably implemented as a softwaremodule operating on the computer 108. The composite analysis element 214may be implemented, however, in any appropriate configuration, such asin a hardware solution or a combined hardware and software solution.Further, the composite analysis element 214 may be executed as usingsoftware executed on the test system computer 108 or a remote computer,such as an independent workstation or a third party's separate computersystem. The composite analysis may be performed at run time, followingthe generation of one or more complete datasets, or upon a collection ofdata generated well before the composite analysis, including historicaldata.

The composite analysis may use any data from two or more datasets.Composite analysis can receive several sets of input data, including rawdata and filtered or smoothed data, for each wafer, such as by executingmultiple configurations through the classification engine. Oncereceived, the input data is suitably filtered using a series ofuser-defined rules, which can be defined as mathematical expressions,formulas, or any other criteria. The data is then analyzed to identifypatterns or irregularities in the data. The composite data may also bemerged into other data, such as the raw data or analyzed data, togenerate an enhanced overall dataset. The composite analysis element 214may then provide an appropriate output report that may be used toimprove the test process. For example, the output report may provideinformation relating to issues in a manufacturing and/or testingprocess.

In the present system, the composite analysis element 214 analyzes setsof wafer data in conjunction with user expressions or other suitableprocesses and a spatial analysis to build and establish composite mapsthat illustrate significant patterns or trends. Composite analysis canreceive several different datasets and/or composite maps for any one setof wafers by executing multiple user configurations on the set ofwafers.

Referring to FIG. 13, in the present embodiment in the semiconductortesting environment, the composite analysis element 214 receives datafrom multiple datasets, such as the data from multiple wafers or lots(1310). The data may comprise any suitable data for analysis, such asraw data, filtered data, smoothed data, historical data from prior testruns, or data received from the tester at run time. In the presentembodiment, the composite analysis element 214 receives raw data andfiltered data at run time. The filtered data may comprise any suitabledata for analysis, such as smoothed data and/or signature analysis data.In the present embodiment, the composite analysis element 214 receivesthe raw dataset and supplementary data generated by the supplementarydata analysis element 206, such as the smoothed data, identification offailures, identification of outliers, signature analysis data, and/orother data.

After receiving the raw data and the supplementary data, the compositeanalysis element 214 generates composite data for analysis (1312). Thecomposite data comprises data representative of information from morethan one dataset. For example, the composite data may comprise summaryinformation relating to the number of failures and/or outliers for aparticular test occurring for corresponding test data in differentdatasets, such as data for components at identical or similar positionson different wafers or in different lots. The composite data may,however, comprise any appropriate data, such as data relating to areashaving concentrations of outliers or failures, wafer locationsgenerating a significant number of outliers or failures, or other dataderived from two or more datasets.

The composite data is suitably generated by comparing data from thevarious datasets to identify patterns and irregularities among thedatasets. For example, the composite data may be generated by ananalysis engine configured to provide and analyze the composite dataaccording to any suitable algorithm or process. In the presentembodiment, the composite analysis element 214 includes a proximityengine configured to generate one or more composite masks based on thedatasets. The composite analysis element 214 may also process thecomposite mask data, for example to refine or emphasize information inthe composite mask data.

In the present embodiment, the proximity engine receives multipledatasets, either through a file, memory structure, database, or otherdata store, performs a spatial analysis on those datasets (1320), andoutputs the results in the form of a composite mask. The proximityengine may generate the composite mask data, such as a composite imagefor an overall dataset, according to any appropriate process ortechnique using any appropriate methods. In particular, the proximityengine suitably merges the composite data with original data (1322) andgenerates an output report for use by the user or another system (1324).The proximity engine may also be configured to refine or enhance thecomposite mask data for analysis, such as by spatial analysis, analyzingthe data for recurring characteristics in the datasets, or removing datathat does not meet selected criteria.

In the present embodiment, the proximity engine performs composite maskgeneration 1312, and may also be configured to determine exclusion areas1314, perform proximity weighting 1316, and detect and filter clusters1318. The proximity engine may also provide proximity adjustment orother operations using user-defined rules, criteria, thresholds, andprecedence. The result of the analysis is a composite mask of theinputted datasets that illustrates spatial trends and/or patterns foundthroughout the datasets given. The proximity engine can utilize anyappropriate output method or medium, including memory structures,databases, other applications, and file-based data stores such as textfiles or XML files in which to output the composite mask.

The proximity engine may use any appropriate technique to generate thecomposite mask data, including cumulative squared methods, N-pointsformulas, Western Electrical rules, or other user defined criteria orrules. In the present embodiment, composite mask data may be consideredas an overall encompassing or “stacked” view of multiple datasets. Thepresent proximity engine collects and analyzes data for correspondingdata from multiple datasets to identify potential relationships orcommon characteristics in the data for the particular set ofcorresponding data. The data analyzed may be any appropriate data,including the raw data, smoothed data, signature analysis data, and/orany other suitable data.

In the present embodiment, the proximity engine analyzes data forcorresponding locations on multiple wafers. Referring to FIG. 14, eachwafer has devices in corresponding locations that may be designatedusing an appropriate identification system, such as an x, y coordinatesystem. Thus, the proximity engine compares data for devices atcorresponding locations or data points, such as location 10, 12 as shownin FIG. 14, to identify patterns in the composite set of data.

The proximity engine of the present embodiment uses at least one of twodifferent techniques for generating the composite mask data, acumulative squared method and a formula-based method. The proximityengine suitably identifies data of interest by comparing the data toselected or calculated thresholds. In one embodiment, the proximityengine compares the data points at corresponding locations on thevarious wafers and/or lots to thresholds to determine whether the dataindicate potential patterns across the datasets. The proximity enginecompares each datum to one or more thresholds, each of which may beselected in any appropriate manner, such as a predefined value, a valuecalculated based on the current data, or a value calculated fromhistorical data.

For example, a first embodiment of the present proximity engineimplements a cumulative squared method to compare the data tothresholds. In particular, referring to FIG. 15, the proximity enginesuitably selects a first data point (1512) in a first dataset (1510),such as a result for a particular test for a particular device on aparticular wafer in a particular lot, and compares the data point valueto a count threshold (1514). The threshold may comprise any suitablevalue, and any type of threshold, such as a range, a lower limit, anupper limit, and the like, and may be selected according to anyappropriate criteria. If the data point value exceeds the threshold,i.e., is lower than the threshold, higher than the threshold, within thethreshold limits, or whatever the particular qualifying relationship maybe, an absolute counter is incremented (1516) to generate a summaryvalue corresponding to the data point.

The data point value is also compared to a cumulative value threshold(1518). If the data point value exceeds the cumulative value threshold,the data point value is added to a cumulative value for the data point(1520) to generate another summary value for the data point. Theproximity engine repeats the process for every corresponding data point(1522) in every relevant dataset (1524), such as every wafer in the lotor other selected group of wafers. Any other desired tests orcomparisons may be performed as well for the data points and datasets.

When all of the relevant data points in the population have beenprocessed, the proximity engine may calculate values based on theselected data, such as data exceeding particular thresholds. Forexample, the proximity engine may calculate overall cumulativethresholds for each set of corresponding data based on the cumulativevalue for the relevant data point (1526). The overall cumulativethreshold may be calculated in any appropriate manner to identifydesired or relevant characteristics, such as to identify sets ofcorresponding data points that bear a relationship to a threshold. Forexample, the overall cumulative threshold (Limit) may be definedaccording to the following equation:

${Limit} = {{Average} + \frac{\left( {{ScaleFactor}*{Standard}\mspace{14mu}{Deviation}^{2}} \right)}{\left( {{Max} - {Min}} \right)\mspace{194mu}}}$

where Average is the mean value of the data in the composite populationof data, Scale Factor is a value or variable selected to adjust thesensitivity of the cumulative squared method, Standard Deviation is thestandard deviation of data point values in the composite population ofdata, and (Max−Min) is the difference between the highest and lowestdata point values in the complete population of data. Generally, theoverall cumulative threshold is defined to establish a comparison valuefor identifying data points of interest in the particular data set.

Upon calculation of the overall cumulative threshold, the proximityengine determines whether to designate each data point for inclusion inthe composite data, for example by comparing the count and cumulativevalues to thresholds. The proximity engine of the present embodimentsuitably selects a first data point (1528), squares the total cumulativevalue for the data point (1530), and compares the squared cumulativevalue for the data point to the dynamically generated overall cumulativethreshold (1532). If the squared cumulative value exceeds the overallcumulative threshold, then the data point is designated for inclusion inthe composite data (1534).

The absolute counter value for the data point may also be compared to anoverall count threshold (1536), such as a pre-selected threshold or acalculated threshold based on, for example, a percentage of the numberof wafers or other datasets in the population. If the absolute countervalue exceeds the overall count threshold, then the data point may againbe designated for inclusion in the composite data (1538). The process issuitably performed for each data point (1540).

The proximity engine may also generate the composite mask data usingother additional or alternative techniques. The present proximity enginemay also utilize a formula-based system for generating the compositemask data. A formula-based system according to various aspects of thepresent invention uses variables and formulas, or expressions, to definea composite wafer mask.

For example, in an exemplary formula-based system, one or more variablesmay be user-defined according to any suitable criteria. The variablesare suitably defined for each data point in the relevant group. Forexample, the proximity engine may analyze each value in the datapopulation for the particular data point, for example to calculate avalue for the data point or count the number of times a calculationprovides a particular result. The variables may be calculated for eachdata point in the dataset for each defined variable.

After calculating the variables, the data points may be analyzed, suchas to determine whether the data point meets the user-defined criteria.For example, a user-defined formula may be resolved using the calculatedvariable values, and if the formula equates to a particular value orrange of values, the data point may be designated for inclusion in thecomposite mask data.

Thus, the proximity engine may generate a set of composite mask dataaccording to any suitable process or technique. The resulting compositemask data comprises a set of data that corresponds to the results of thedata population for each data point. Consequently, characteristics forthe data point may be identified over multiple datasets. For example, inthe present embodiment, the composite mask data may illustrateparticular device locations that share characteristics on multiplewafers, such as widely variable test results or high failure rates. Suchinformation may indicate issues or characteristics in the manufacturingor design process, and thus may be used to improve and controlmanufacturing and testing.

The composite mask data may also be analyzed to generate additionalinformation. For example, the composite mask data may be analyzed toillustrate spatial trends and/or patterns in the datasets and/oridentify or filter significant patterns, such as filtering to reduceclutter from relatively isolated data points, enhancing or refiningareas having particular characteristics, or filtering data having knowncharacteristics. The composite mask data of the present embodiment, forexample, may be subjected to spatial analyses to smooth the compositemask data and complete patterns in the composite mask data. Selectedexclusion zones may receive particular treatment, such that compositemask data may be removed, ignored, enhanced, accorded lowersignificance, or otherwise distinguished from other composite mask data.A cluster detection process may also remove or downgrade the importanceof data point clusters that are relatively insignificant or unreliable.

In the present embodiment, the proximity engine may be configured toidentify particular designated zones in the composite mask data suchthat data points from the designated zones are accorded particulardesignated treatment or ignored in various analyses. For example,referring to FIG. 16, the proximity engine may establish an exclusionzone at a selected location on the wafers, such as individual devices,groups of devices, or a band of devices around the perimeter of thewafer. The purpose of the exclusion zone is to provide a mechanism toexclude certain data points from affecting other data points inproximity analysis and/or weighting. The data points are designated asexcluded in any suitable manner, such as by assigning values that areout of the range of subsequent processes.

The relevant zone may be identified in any suitable manner. For example,excluded data points may be designated using a file listing of deviceidentifications or coordinates, such as x,y coordinates, selecting aparticular width of band around the perimeter, or other suitable processfor defining a relevant zone in the composite data. In the presentembodiment, the proximity engine may define a band of excluded deviceson a wafer using a simple calculation that causes the proximity engineto ignore or otherwise specially treat data points within a user definedrange of the edge of the data set. For example, all devices within thisrange, or listed in the file, are then subject to selected exclusioncriteria. If the exclusion criteria are met, the data points in theexclusion zone or the devices meeting the exclusion criteria areexcluded from one or more analyses.

The proximity engine of the present embodiment is suitably configured toperform additional analyses upon the composite mask data. The additionalanalyses may be configured for any appropriate purpose, such as toenhance desired data, remove unwanted data, or identify selectedcharacteristics in the composite mask data. For example, the proximityengine is suitably configured to perform a proximity weighting process,such as based on a point weighting system, to smooth and completepatterns in the composite mask data.

Referring to FIGS. 17A-B and 18, the present proximity engine searchesthrough all data points in the dataset (1730). The proximity engineselects a first point (1710) and cheeks the value of the data pointagainst a criterion, such as a threshold or a range of values (1712).When a data point is found that exceeds the selected threshold or iswithin the selected range, the proximity engine searches data pointssurrounding the main data point for values (1714). The number of datapoints around the main data point may be any selected number, and may heselected according to any suitable criteria.

The proximity engine searches the surrounding data points for datapoints exceeding an influence value or satisfying another suitablecriterion indicating that the data point should be accorded weight(1716). If the data point exceeds the influence value, the proximityengine suitably assigns a weight to the main data point according to thevalues of the surrounding data points. In addition, the proximity enginemay adjust the weight according to the relative position of thesurrounding datapoint. For example, the amount of weight accorded to asurrounding data point can be determined according to whether thesurrounding data point is adjacent (1718) or diagonal (1720) to the maindata point. The total weight may also be adjusted if the data point ison the edge of the wafer (1722). When all surrounding data points aroundthe main data point have been checked (1724), the main data point isassigned an overall weight, for example by adding the weight factorsfrom the surrounding data points. The weight for the main data point maythen be compared to a threshold, such as a user defined threshold(1726). If the weight meets or exceeds the threshold, the data point isso designated (1728).

The composite mask data may also be further analyzed to filter data. Forexample, in the present embodiment, the proximity engine may beconfigured to identify, and suitably remove, groups of data points thatare smaller than a threshold, such as a user-specified threshold.

Referring to FIGS. 19 and 20, the proximity engine of the presentembodiment may be configured to define the groups, size them, and removesmaller groups. To define the groups, the proximity engine searchesthrough every data point (1922) in the composite mask data for a datapoint satisfying a criterion. For example, the data points in thecomposite mask data may be separated into ranges of values and assignedindex numbers. The proximity engine begins by searching the compositemask data for a data point matching a certain index (1910). Uponencountering a data point meeting designated index (1912), the proximityengine designates the found point as the main data point and initiates arecursive program that searches in all directions from the main datapoint for other data points that are in the same index, oralternatively, have substantially the same value, also exceed thethreshold, or meet other desired criteria (1914).

As an example of a recursive function in the present embodiment, theproximity engine may begin searching for data points having a certainvalue, for instance five. If a data point with a value of five is found,the recursive program searches all data points around the main deviceuntil it finds another data point with the value of five. If anotherqualifying data point is found, the recursive program selects theencountered data point as the main data point and repeats the process.Thus, the recursive process analyzes and marks all data points havingmatching values that are adjacent or diagonal to each other and thusform a group. When the recursive program has found all devices in agroup having a certain value, the group is assigned a unique group indexand the proximity engine again searches through the entire compositemask data. When all of the data values have been searched, the compositemask data is fully separated into groups of contiguous data pointshaving the same group index.

The proximity engine may determine the size of each group. For example,the proximity engine may count the number of data points in the group(1916). The proximity engine may then compare the number of data pointsin each group to a threshold and remove groups that do not meet thethreshold (1918). The groups may be removed from the grouping analysisin any suitable manner (1920), such as by resetting the index value forthe relevant group to a default value. For example, if the thresholdnumber of data points is five, the proximity engine changes the groupindex number for every group having fewer than five data points to adefault value. Consequently, the only groups that remain classified withdifferent group indices are those having five or more data points.

The proximity engine may perform any appropriate additional operationsto generate and refine the composite mask data. For example, thecomposite mask data, including the results of the additional filtering,processing, and analyzing of the original composite mask data, may beused to provide information relating to the multiple datasets and theoriginal source of the data, such as the devices on the wafers or thefabrication process. The data may be provided to the user or otherwiseused in any suitable manner. For example, the data may be provided toanother system for further analysis or combination with other data, suchas executing user-defined rules combined with a merging operation on thecomposite mask data, the raw data, and any other appropriate data toproduce a data set that represents trends and patterns in the raw data.Further, the data may be provided to a user via an appropriate outputsystem, such as a printer or visual interface.

In the present embodiment, for example, the composite mask data iscombined with other data and provided to the user for review. Thecomposite mask data may be combined with any other appropriate data inany suitable manner. For example, the composite mask data may be mergedwith signature data, the raw data, hardware bin data, software bin data,and/or other composite data. The merging of the datasets may beperformed in any suitable manner, such as using various user-definedrules including expressions, thresholds, and precedence.

In the present system, the composite analysis element 214 performs themerging process using an appropriate process to merge the composite maskdata with an original map of composite data, such as a map of compositeraw data, composite signature data, or composite bin data. For example,referring to FIG. 21, the composite analysis element 214 may merge thecomposite mask data with the original individual wafer data using anabsolute merge system in which the composite mask data is fully mergedwith the original data map. Consequently, the composite mask data ismerged with the original data map regardless of overlap or encompassmentof existing patterns. If only one composite mask illustrates a patternout of multiple composite masks, the pattern is included in the overallcomposite mask.

Alternatively, the composite analysis element 214 may merge the data inconjunction with additional analysis. The composite analysis element 214may filter data that may be irrelevant or insignificant. For example,referring to FIG. 22, the composite analysis element 214 may merge onlydata in the composite mask data that overlaps data in the original datamap or in another composite mask, which tends to emphasize potentiallyrelated information.

The composite analysis element 214 may alternatively evaluate thecomposite mask data and the original data to determine whether aparticular threshold number, percentage, or other value of data pointsoverlap between maps. Depending on the configuration, the data mergedmay only include areas where data points, in this case corresponding todevices, overlapped sufficiently between the composite mask data and theoriginal data to meet the required threshold value for overlapping datapoints. Referring to FIG. 23, the composite analysis element 214 may beconfigured to include only composite data patterns that overlaps to asufficient degree with tester bin failures, i.e., failed devices, in theoriginal data, such as 50% of the composite data overlapping with testerbin failure data. Thus, if less than the minimum amount of compositedata overlaps with the original data, the composite data pattern may beignored. Similarly, referring to FIG. 24, the composite analysis element214 may compare two different sets of composite data, such as data fromtwo different recipes, and determine whether the overlap between the tworecipes satisfies selected criteria. Only the data that overlaps and/orsatisfies the minimum criteria is merged.

The merged data may be provided to an output element for output to theuser or other system. The merged data may be passed as input to anotherprocess, such as a production error identification process or a largetrend identification process. The merged data may also be outputted inany assortment or format, such as memory structures, database tables,flat text files, or XML files.

In the present embodiment, the merged data and/or wafer maps areprovided into an ink map generation engine. The ink map engine producesmaps for offline inking equipment. In addition to offline inking maps,the merged data results may be utilized in generating binning resultsfor inkless assembly of parts, or any other process or application thatutilizes these types of results.

The particular implementations shown and described are merelyillustrative of the invention and its best mode and are not intended tootherwise limit the scope of the present invention in any way. For thesake of brevity, conventional signal processing, data transmission, andother functional aspects of the systems (and components of theindividual operating components of the systems) may not be described indetail. Furthermore, the connecting lines shown in the various figuresare intended to represent exemplary functional relationships and/orphysical couplings between the various elements. Many alternative oradditional functional relationships or physical connections may bepresent in a practical system. The present invention has been describedabove with reference to a preferred embodiment. Changes andmodifications may be made, however, without departing from the scope ofthe present invention. These and other changes or modifications areintended to be included within the scope of the present invention, asexpressed in the following claims.

1. A test system, comprising: a storage system configured to store firsttest data for a first individual component and second test data for asecond individual component, wherein the first and second componentsoccupy corresponding locations on different wafers; and a compositeanalysis element configured to analyze the first test data and thesecond test data for common characteristics.
 2. A test system accordingto claim 1, wherein the composite analysis element is configured tocompare a summary value for the first and second test data to athreshold.
 3. A test system according to claim 2, wherein the thresholdis a dynamic threshold based on the test data.
 4. A test systemaccording to claim 2, wherein the summary value is determined accordingto a number of test data for corresponding components to satisfy acriterion.
 5. A test system according to claim 1, wherein the compositeanalysis element is configured to compare the first test data and thesecond test data to a threshold.
 6. A test system according to claim 5,wherein the threshold is calculated based on the first test data and thesecond test data.
 7. A test system according to claim 1, wherein thecommon characteristics comprise data values meeting at least onethreshold.
 8. A test system according to claim 1, wherein the compositeanalysis element is configured to generate composite data according tothe test data having the common characteristics.
 9. A test systemaccording to claim 8, wherein the composite analysis element isconfigured to accord a designated treatment to composite data within adesignated zone.
 10. A test system according to claim 9, wherein thecomposite analysis element is configured to at least one of ignore andaccord a lower significance to the composite data within the designatedzone.
 11. A test system according to claim 8, wherein the compositeanalysis element is configured to perform a spatial analysis on thecomposite data.
 12. A test system according to claim 8, wherein thecomposite analysis element is configured to accord a significance to aselected composite data point based on values of nearby composite datapoints.
 13. A test system according to claim 12, wherein thesignificance accorded to the selected composite data point is adjustedby a first amount if a first nearby composite data point is at a firstposition relative to the selected composite data point and by a secondamount if the first nearby composite data point is at a second positionrelative to the selected composite data point.
 14. A test systemaccording to claim 8, wherein the composite analysis element isconfigured to remove a cluster of composite data points from thecomposite data.
 15. A test system according to claim 14, wherein thecomposite analysis element is configured to remove the cluster ofcomposite data points only when the cluster is smaller than a selectedsize.
 16. A test system according to claim 8, wherein the compositeanalysis element is configured to merge the composite data with anotherset of data.
 17. A test system according to claim 16, further comprisingan output element configured to generate a composite map including themerged composite data.
 18. A test system, comprising: a storage systemconfigured to store test data for at least two sets of components,including test data for individual components, wherein: components fromeach set of components correspond to components in other sets ofcomponents; and the corresponding components occupy correspondinglocations on different wafers; and a composite analysis elementconfigured to analyze the test data for at least two individualcorresponding components for common characteristics.
 19. A test systemaccording to claim 18, wherein the composite analysis element isconfigured to compare a summary value for at least one test datum forone or more corresponding components to a threshold.
 20. A test systemaccording to claim 19, wherein the threshold is a dynamic thresholdbased on the test data.
 21. A test system according to claim 19, whereinthe summary value is determined according to a number of test data forcorresponding components to satisfy a criterion.
 22. A test systemaccording to claim 18, wherein the composite analysis element isconfigured to compare the test data from different datasets to athreshold.
 23. A test system according to claim 22, wherein thethreshold is calculated based on the test data.
 24. A test systemaccording to claim 18, wherein the common characteristics comprise datavalues meeting at least one threshold.
 25. A test system according toclaim 18, wherein the composite analysis element is configured togenerate composite data according to the test data having the commoncharacteristics.
 26. A test system according to claim 25, wherein thecomposite analysis element is configured to accord a designatedtreatment to composite data within a designated zone.
 27. A test systemaccording to claim 26, wherein the composite analysis element isconfigured to at least one of ignore and accord a lower significance tothe composite data within the designated zone.
 28. A test systemaccording to claim 25, wherein the composite analysis element isconfigured to perform a spatial analysis on the composite data.
 29. Atest system according to claim 25, wherein the composite analysiselement is configured to accord a significance to a selected compositedata point based on values of nearby composite data points.
 30. A testsystem according to claim 29, wherein the significance accorded to theselected composite data point is adjusted by a first amount if a firstnearby composite data point is at a first position relative to theselected composite data point and by a second amount if the first nearbycomposite data point is at a second position relative to the selectedcomposite data point.
 31. A test system according to claim 25, whereinthe composite analysis element is configured to remove a cluster ofcomposite data points from the composite data.
 32. A test systemaccording to claim 31, wherein the composite analysis element isconfigured to remove the cluster of composite data points only when thecluster is smaller than a selected size.
 33. A test system according toclaim 25, wherein the composite analysis element is configured to mergethe composite data with another set of data.
 34. A test system accordingto claim 33, further comprising an output element configured to generatea composite map including the merged composite data.
 35. A method fortesting semiconductors, comprising: obtaining at least two datasets oftest data for at least two sets of components, wherein: a firstcomponent in a first set corresponds to a second component in a secondset; and the first and second components occupy corresponding locationson different wafers; and analyzing the test data for the individualcorresponding first and second components for common characteristics.36. A method for testing according to claim 35, wherein analyzing thetest data comprises comparing a summary value based on test data formultiple components to a threshold.
 37. A method for testing accordingto claim 36, wherein the threshold is a dynamic threshold based on thetest data.
 38. A method for testing according to claim 36, wherein thesummary value is determined according to a number of test data forcorresponding components to satisfy a criterion.
 39. A method fortesting according to claim 35, wherein the composite analysis element isconfigured to compare the test data from different datasets to athreshold.
 40. A method for testing according to claim 39, wherein thethreshold is calculated based on the test data.
 41. A method for testingaccording to claim 35, wherein the common characteristics comprise datavalues meeting at least one threshold.
 42. A method for testingaccording to claim 35, further comprising generating composite dataaccording to the test data having the common characteristics.
 43. Amethod for testing according to claim 42, wherein generating compositedata includes according a designated treatment to composite data withina designated zone.
 44. A method for testing according to claim 43,wherein according the designated treatment includes at least one ofignoring and according a lower significance to the composite data withinthe designated zone.
 45. A method for testing according to claim 42,further comprising performing a spatial analysis on the composite data.46. A method for testing according to claim 42, further comprisingaccording a significance to a selected composite data point based onvalues of nearby composite data points.
 47. A method for testingaccording to claim 46, wherein the significance accorded to the selectedcomposite data point is adjusted by a first amount if a first nearbycomposite data point is at a first position relative to the selectedcomposite data point and by a second amount if the first nearbycomposite data point is at a second position relative to the selectedcomposite data point.
 48. A method for testing according to claim 42,further comprising removing a cluster of composite data points from thecomposite data.
 49. A method for testing according to claim 48, whereinremoving the cluster includes removing the cluster of composite datapoints only when the cluster is smaller than a selected size.
 50. Amethod for testing according to claim 42, further comprising merging thecomposite data with another set of data.
 51. A method for testingaccording to claim 50, further comprising generating a composite mapincluding the merged composite data.